Temporal Feature Engineering and Ensemble Learning for Predicting 28-Day Mortality in ICU Patients with Alcoholic Cirrhosis
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
Predicting 28-day mortality in ICU patients with alcoholic cirrhosis is challenging because clinical deterioration is dynamic and heterogeneous.
Methods
Using MIMIC-IV (v3.1), this study included 1,907 patients (training n = 1 , 334 ; validation n = 573 ), engineering 208 temporal and static predictors from 64 base variables and reducing them to 40 through multi-stage selection. Seven classifiers and a weighted gradient-boosting ensemble (XGBoost, CatBoost, LightGBM) were compared with Optuna tuning.
Results
The ensemble achieved the highest internal validation AUC (0.9276; 95% CI: 0.9011–0.9507) and lowest Brier score (0.0870), with strong discrimination on eICU-CRD (AUC 0.9347) and related MIMIC-III (AUC 0.9071). Ablation indicated that temporal features, especially deltas, were major contributors ( Δ AUC ≈ 0.17 when removed). SHAP highlighted APS III score, anion gap, oxygen saturation (delta), lactate, and INR as leading predictors.
Conclusions
The framework supports interpretable, trajectory-informed risk stratification in critically ill cirrhotic patients; prospective validation is needed before clinical use.
